Abstract
Artificial gene regulatory networks are computational models which draw inspiration from real world networks of biological gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper introduces a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. The results demonstrate that the AERNs are more adept at controlling multiple opposing trajectories within Chirikov’s standard map, suggesting that AERNs are an interesting area for further investigation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alberts, B., Bray, D., Lewis, J., Raff, M., Roberts, K., Watson, J.: Molecular Biology of the Cell, 3rd edn. Oxford Univ. Press (1994)
Allis, C., Jenuwein, T., Reinberg, D.: Epigenetics. Cold Spring Harbor Laboratory Press (2007)
Bird, A.: DNA methylation patterns and epigenetic memory. Genes & Development 16(1), 6 (2002)
Bird, A.: Perceptions of epigenetics. Nature 447(7143), 396 (2007)
Bollt, E., Meiss, J.: Controlling chaotic transport through recurrence. Physica D: Nonlinear Phenomena 81(3), 280–294 (1995)
Bushman, F.: Lateral DNA transfer: mechanisms and consequences. Cold Spring Harbor Laboratory Press (2002)
Cedar, H., Bergman, Y.: Linking DNA methylation and histone modification: patterns and paradigms. Nat. Rev. Genet. 10(5), 295–304 (2009)
Chirikov, B., Sanders, A.: Research concerning the theory of non-linear resonance and stochasticity. Nuclear Physics Institute of the Siberian Section of the USSR Academy of Sciences (1971)
Fraser, C., Gocayne, J., White, O., Adams, M., Clayton, R., Fleischmann, R., Bult, C., Kerlavage, A., Sutton, G., Kelley, J., et al.: The minimal gene complement of mycoplasma genitalium. Science 270(5235), 397 (1995)
Harvey, I., Bossomaier, T.: Time out of joint: Attractors in asynchronous random boolean networks. In: Proceedings of the Fourth European Conference on Artificial Life, pp. 67–75. MIT Press, Cambridge (1997)
Hattman, S.: Dna-[adenine] methylation in lower eukaryotes. Biochemistry 70(5), 550–558 (2005)
Jackson, J., Lindroth, A., Cao, X., Jacobsen, S.: Control of CpNpG DNA methylation by the kryptonite histone h3 methyltransferase. Nature 416(6880), 556–560 (2002)
Jeanteur, P.: Epigenetics and Chromatin. Progress in Molecular and Subcellular Biology. Springer, Heidelberg (2008)
Kauffman, S., Peterson, C., Samuelsson, B., Troein, C.: Random Boolean network models and the yeast transcriptional network. Proceedings of the National Academy of Sciences of the United States of America 100(25), 14796–14799 (2003)
Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology 22(3), 437–467 (1969)
Kumar, S.: The evolution of genetic regulatory networks for single and multicellular development. In: GECCO. Citeseer (2004)
Latchman, D.S.: Gene regulation: a eukaryotic perspective. Advanced text. Taylor & Francis (2005)
Lones, M.A., Tyrrell, A.M., Stepney, S., Caves, L.S.: Controlling Complex Dynamics with Artificial Biochemical Networks. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 159–170. Springer, Heidelberg (2010)
Mitchell, M.: An introduction to genetic algorithms. Complex adaptive systems. MIT Press (1998)
Mohn, F., Schübeler, D.: Genetics and epigenetics: stability and plasticity during cellular differentiation. Trends in Genetics 25(3), 129–136 (2009)
Quick, T., Nehaniv, C.L., Dautenhahn, K., Roberts, G.: Evolving Embodied Genetic Regulatory Network-Driven Control Systems. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 266–277. Springer, Heidelberg (2003)
Tél, T., Gruiz, M.: Chaotic dynamics: an introduction based on classical mechanics. Cambridge University Press (2006)
Turner, B.: Chromatin and gene regulation: mechanisms in epigenetics. Blackwell Science (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Turner, A.P., Lones, M.A., Fuente, L.A., Stepney, S., Caves, L.S., Tyrrell, A.M. (2012). Using Artificial Epigenetic Regulatory Networks to Control Complex Tasks within Chaotic Systems. In: Lones, M.A., Smith, S.L., Teichmann, S., Naef, F., Walker, J.A., Trefzer, M.A. (eds) Information Processign in Cells and Tissues. IPCAT 2012. Lecture Notes in Computer Science, vol 7223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28792-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-28792-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28791-6
Online ISBN: 978-3-642-28792-3
eBook Packages: Computer ScienceComputer Science (R0)